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Green scheduling of job shop considering AGV transportation and machine speed
Published:2024-03-06 author:LI Zhengfeng, ZHANG Dongfang, DING Qicong, et al. Browse: 1287 Check PDF documents
Green scheduling of job shop considering AGV transportation and 
machine speed

LI Zhengfeng1, ZHANG Dongfang1, DING Qicong1, ZHANG Guohui2
(1.School of Mechatronics Engineering, Zhongyuan University of Technology, Zhengzhou 450007, China;
2.School of Management Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450015, China)

Abstract:  In actual production in the workshop, energy of machine will be consumed not only in the processing state, but also in the no-load state, and the energy consumption of machines will vary at different processing speed. In addition, with the wide application of automated guided vehicle (AGV) in workshops, its energy consumption can not be ignored, therefore the research on green scheduling of job shops considering AGV and machine speed is of great significance. Makespan is not only regarded as the efficiency indicator of workshop production, but also, to a certain extent, means the reduction indicator of machine energy consumption in no-load state and the improvement indicator of machine utilization. Based on this, the makespan and energy consumption optimization problem of the job shop considering AGV transportation and machine speed was studied. Firstly, the machine energy consumption and AGV energy consumption of job shop were analyzed, and the process constraints among AGV, job and machine were explored. And the green job shop scheduling model considering AGV transportation and machine speed was established. Then, the two-stage optimization method was adopted to optimize makespan and energy consumption of job shop. In the first stage, the machine processed at the highest speed and used an improved sparrow search algorithm to optimize makespan; in the second stage, while the makespan optimized in the first stage remained unchanged, the gap gear adjustment strategy was proposed to reduce energy consumption in job shop by reducing the machine speed of some processes to achieve optimization of the total energy consumption in job shop. Finally, the algorithms were simulated and verified using standard use cases through simulation. The experimental results show that in the first stage, the improved sparrow search algorithm can improve the quality of the population, accelerate the convergence speed of the algorithm, jump out of the local optimal solution, and obtain better makespan. In the second stage, on the basis of above makespan, the gap adjustment strategy is adopted to reduce the energy consumption, and the energy consumption can be reduced by 1.13% ~5.18% through 10 use cases. The results verify the correctness of the green scheduling model considering AGV and machine speed and the effectiveness of the improved sparrow algorithm.
Key words: job shop completion time; energy consumption optimization problem; green scheduling; sparrow search algorithm; machine speed; gap adjustment strategy; automated guided vehicle(AGV)
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